124,974 research outputs found
The minimum-SER linear-combiner decision feedback equalizer
No full textThe paper considers the conventional decision feedback equalizer (DFE) that employs a linear combination of the channel observations and the past decisions. An expression of the symbol error rate (SER) is derived for the linear-combiner DFE with the general -PAM constellation by utilizing a geometric translation property of decision feedback. A method is developed to optimize the coefficients of the linear-combiner DFE to achieve the minimum-SER (MSER) solution. The performance of this MSER linear-combiner DFE is superior to the usual minimum mean square error (MMSE) solution
Asymptotic Bayesian Decision Feedback Equalizer Using a Set of Hyperplanes
No full textWe present a signal space partitioning technique for realizing the optimal Bayesian decision feedback equalizer (DFE). It is known that, when the signal to noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. The proposed technique determines these hyperplanes explicitly and uses them to partition the observation signal space. The resulting equalizer is made up of a set of parallel linear discriminant functions and a Boolean mapper. Unlike the existing signal space partitioning technique of Kim and Moon, which involves complex combinatorial search and optimization in design, our design procedure is simple and straightforward, and guarantees to achieve the asymptotic Bayesian DFE
Adaptive minimum symbol-error-rate decision feedback equalization for multilevel pulse-amplitude modulation
No full textThe design of decision feedback equalizers (DFEs) is typically based on the minimum mean square error (MMSE) principle, as this leads to effective adaptive implementation in the form of the least mean square algorithm. It is well-known, however, that in certain situations the MMSE solution can be distinctly inferior to the optimal minimum symbol error rate (MSER) solution. We consider the MSER design for multi-level pulse-amplitude modulation. Block-data adaptive implementation of the theoretical MSER DFE solution is developed based on the Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive MSER algorithm, called the least symbol error rate (LSER), is derived for adaptive equalization application. The proposed LSER algorithm has a complexity that increases linearly with the equalizer length. Computer simulation is employed to evaluate the proposed alternative MSER design for equalization application with multi-level signalling schemes
Multiple hyperplane detector for implementing the asymptotic Bayesian decision feedback equalizer
No full textA detector based on multiple-hyperplane partitioning of the signal space is derived for realizing the Bayesian decision feedback equaliser (DFE). It is known that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the signal to noise ratio (SNR) tends to infinity. The proposed technique determines these hyperplanes and uses them to partition the observation space. The resulting detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision complexity
Mulgrew, B J, VX36973
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/406605Surname: MULGREW. Given Name(s) or Initials: B J. Military Service Number or Last Known Location: VX36973. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 16392.248020
Item: [2016.0049.38882] "Mulgrew, B J, VX36973
A clustering technique for digital communications channel equalization using radial basis function networks
No full textAdaptive Minimum-BER Linear Multiuser Detection for DS-CDMA Signals in Multipath Channels
No full textThe problem of constructing adaptive minimum bit error rate (MBER) linear multiuser detectors is considered for direct-sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style stochastic gradient adaptive algorithm is developed for training linear multiuser detectors. Computer simulation is used to study the convergence speed and steady-state BER misadjustment of this adaptive MBER linear multiuser detector, and the results show that it outperforms an existing LMS-style adaptive MBER algorithm first presented at Globecom'98 by Yeh, Lopes and Barry
Adaptive Bayesian equalizer with decision feedback
No full textA Bayesian solution is derived for digital communication channel equalization with decision feedback. This is an extension to the maximum a posteriori probability symbol-decision equalizer to include decision feedback. A novel scheme of utilizing decision feedback is proposed which not only improves equalization performance but also reduces computational complexity dramatically. It is shown that the Bayesian equalizer has an equivalent structure to the radial basis function network, the latter being a one-hidden-layer artificial neural network widely used in pattern classification and many other areas of signal processing. Two adaptive approaches are developed to realize the Bayesian solution. The maximum likelihood Viterbi algorithm and the conventional decision feedback equalizer are used as two benchmarks to assess the performance of the Bayesian decision feedback equalizer.</p
Bayesian decision feedback equaliser for overcoming co-channel interference
No full textThe authors derive a Bayesian decision feedback equaliser which incorporates co-channel interference compensation. By exploiting the structure of co-channel interfering signals, the proposed Bayesian decision feedback equaliser is able to distinguish an interfering signal from white noise and utilises this information to improve performance. Adaptive implementation of this Bayesian decision feedback equaliser includes identifying the channel model using the least mean square algorithm and estimating the co-channel states by means of an unsupervised clustering scheme. Simulation involving a binary signal constellation is used to compare both the theoretical and adaptive performance of this Bayesian decision feedback equaliser with those of the maximum likelihood sequence estimator. The results obtained indicate that, in the presence of severe co-channel interference, the Bayesian decision feedback equaliser employing the proposed simple scheme to compensate co-channel interference can outperform the maximum likelihood sequence estimator that only treats co-channel interference as an additional coloured noise.</p
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